Effects of climate‐change scenarios on the distribution patterns of Castanea henryi

Abstract Castanea henryi, with edible nuts and timber value, is a key tree species playing essential roles in China's subtropical forest ecosystems. However, natural and human perturbations have nearly depleted its wild populations. The study identified the dominant environmental variables enabling and limiting its distribution and predicted its suitable habitats and distribution. The 212 occurrence records covering the whole distribution range of C. henryi in China and nine main bioclimatic variables were selected for detailed analysis. We applied the maximum entropy model (MaxEnt) and QGIS to predict potentially suitable habitats under the current and four future climate‐change scenarios. The limiting factors for distribution were accessed by Jackknife, percent contribution, and permutation importance. We found that the current distribution areas were concentrated in the typical subtropical zone, mainly Central and South China provinces. The modeling results indicated temperature as the critical determinant of distribution patterns, including mean temperature of the coldest quarter, isothermality, and mean diurnal range. Winter low temperature imposed an effective constraint on its spread. Moisture served as a secondary factor in species distribution, involving precipitation seasonality and annual precipitation. Under future climate‐change scenarios, excellent habitats would expand and shift northwards, whereas range contraction would occur on the southern edge. Extreme climate change could bring notable range shrinkage. This study provided a basis for protecting the species' germplasm resources. The findings could guide the management, cultivation, and conservation of C. henryi, assisted by a proposed three‐domain operation framework: preservation areas, loss areas, and new areas, each to be implemented using tailor‐made strategies.


| INTRODUC TI ON
Climate is one of the most critical factors molding the geographical distribution of species and the reproduction and growth of plants (Akyol et al., 2020). Understanding the species' geographical distribution and climatic correlation can grasp the macro-scale pattern of their threatened status, endemism, diversity, evolution, and development and offer hints to predict future distribution . Global warming is causing the fragmentation of suitable habitats for plants. Some species have been driven to the verge of extinction, and some are triggered to migrate to areas with suitable climate conditions (Akyol et al., 2020;Huang et al., 2020).
Therefore, studying the potential distribution of species and the impact of future climate on plant distribution is crucial for timely planning and strategy formulation in species diversity conservation (Li, 2019).
In recent years, some novel GIS-based methods have played an important role in biogeography research (Booth et al., 2014;Fois et al., 2018). They have helped to develop models for predicting species distribution and simulating and visualizing species dispersal and migration pathways (Sarıkaya & Orucu, 2019;Tulowiecki, 2020).
In general, these species distribution models (SDMs) integrate multiple environmental variables affecting the distribution of a species and match its actual distribution points with corresponding environmental variables to assess the species' niche requirements (Booth et al., 2014;Fourcade et al., 2018). The habitat preference is reflected in probability values. Finally, the suitable distribution range of the target species is obtained and depicted on a map (Akyol et al., 2020;Çoban et al., 2020;Huang et al., 2020). With the rapid development of computer and geographic information technologies, many models have been developed to evaluate the correlation between species and environmental variables based on different principles and algorithms. The more commonly adopted SDMs include GARP, BIOCLIM, DOMAIN, CLIMEX, and MaxEnt (Booth et al., 2014;Carpenter et al., 1993;Sarquis et al., 2018).
MaxEnt (maximum entropy) is the most widely used SDM with a high prediction accuracy (Abolmaali et al., 2018;Xie et al., 2021).
The model describes a probability distribution where each grid cell has predicted suitable conditions for a species using a collection of environmental (e.g., climatic) grids and species georeferenced occurrence locations (Steven J. Phillips & Dudík, 2008). The investigation generates the anticipated probability of presence or forecasts local abundance under specific assumptions about the input data and biological sampling operations producing the occurrence records (Fourcade et al., 2018). MaxEnt has been widely used in predicting suitable areas for species growth. In Europe, future climate change may shift tree species range eastward, accompanied by range shrinkage in southern Europe (Puchałka et al., 2021). Other tree species range may shift northward in Europe due to increased climatic seasonality, whereas the southern edge may experience range contraction (Paź-Dyderska et al., 2021). Different populations of the same plant species located at the northern and southern edges of the range could often express opposite reactions to a warming climate (Matías et al., 2014). However, such physiological resilience leading to buffering against range shrinkage may not last with increasing warming (Doak & Morris, 2010). MaxEnt has improved our understanding of the relationship between environmental factors and species growth, occurrence, and spatial changes. Therefore, MaxEnt results can provide hints to enhance the conservation and management of species in a specific region (Manish & Pandit, 2019).
Castanea Mill. is a relatively small genus in the Fagaceae family, with about 12 species mainly distributed in the temperate and subtropical regions of the northern hemisphere (Jones, 1986).
Compared with other Fagaceae genera, Castanea has received limited attention in biogeography research (Deng et al., 2018;Manos & Stanford, 2001). The potential reintroduction of Chestnut to the U.S.A. was assisted by research on its geographic distribution (Dalgleish et al., 2015;Tulowiecki, 2020). SDM analysis permitted the identification of potential restoration sites based on two critical predictors: preference for acidic soil pH and relatively steep slope (Tulowiecki, 2020). Anatolian Chestnut's (C. sativa) potential and future distribution areas in Turkey were predicted to shrink significantly in response to climate change (Sarıkaya & Orucu, 2019).
However, the geographic study of C. henryi has received little attention (Liu & Fang, 2000).
Only three species of Castanea are native to China, including C. mollissima, C. seguinii, and C. henryi (Skan). C. mollissima, with the longest cultivation history, has been studied intensively (Zhang, Wang, et al., 2022). C. seguinii has extremely small nuts and is planted for its edible nuts sometimes also, however not as widely as C. mollissima (Zulfiqar et al., 2019); therefore, studies on it are relatively rare.
C. henryi began to be cultivated in northern Fujian Province (China) 400 years ago, yielding a well-known edible dried nut in southern China (Gong & Chen, 1997). At present, research on C. henryi focuses on germplasm resources, genetic diversity, cultivation techniques, pest control, fruit storage, and processing (Ma et al., 2013).
However, compared with C. mollissima, the research on C. henryi calls for a substantial increase in depth.
Castanea henryi has significant economic and ecological values and is essential in maintaining the function of subtropical forest ecosystems in China (Ma et al., 2013). However, a combination of climate change and human disturbance has negatively impacted its growth and distribution. Human activities have continuously degraded the forest habitats in China to curtail their survival and reproduction (Liang et al., 2018). In addition, climate change has altered its physiological and ecological traits to modify its distribution pattern (Fahad et al., 2021). Field surveys confirmed the rare occurrence of large trees of C. henryi, and the remaining young plants were relatively scarce (Li, Sun, et al., 2018). It tends to be replaced by other broadleaved trees (Li, Sun, et al., 2018;Liang et al., 2018).
We need to deepen our understanding of the factors that promote and limit the natural growth and dispersion of C. henryi. The findings could contribute to its conservation, selection of suitable cultivation sites, and sustainable utilization of the timber tree.
Past studies had shown that climate change posed a serious socioeconomic and ecological risk to Anatolian Chestnut in Portugal; a gradual decrease in Chestnut Suitability Index (CSI) was observed for future climates, especially for RCP8.5 and over the long term (Freitas et al., 2022). Therefore, we assume that a similar situation will also occur in China. Applying the MaxEnt modeling method, the study objectives are to (1) identify and assess the significant climatic factors influencing the current distribution of C. henryi; (2) predict the best regions for future growth of C. henryi based on its current distribution data; and (3) forecast the future climatic trends and find the best regions for C. henryi growth under various climate-change scenarios.

| Collecting species occurrence data
The geographical distribution data of C. henryi were obtained from field investigations, local flora, and database platforms. The databases include the Global Biodiversity Information Facility (GBIF, www.gbif.org), National Specimen Information Infrastructure (NSII, http://www.nsii.org.cn/), and Chinese Virtual Herbarium (CVH, http://www.cvh.ac.cn/). Some data were collected from CNKI (China National Knowledge Infrastructure) and Scopus. A total of 1551 initial records were obtained. They were screened to ascertain whether they were natural or cultivated and to remove the repeated, incomplete, and inaccurate records. To avoid errors caused by the high occurrence density, a sample cell size of 2.5′ grid was adopted.
Using the QGIS software (www.gisag maps.com/qgis-downl oad/), one record closest to each grid's center was kept, and the remaining points were deleted. Finally, 212 high-quality and reliable records were kept ( Figure 1). The records' geographical coordinates were acquired from Google Earth and saved in .csv format. The vector map of China was downloaded from the National Geomatics Center of China (NGCC, http://ngcc.sbsm.gov.cn/).

| Selecting environmental variables
Bioclimatic variables for current and future climate scenarios were extracted from WorldClim (http://www.world clim.org/). More biologically significant variables were derived from bioclimatic factors related to monthly temperature and rainfall records. They were frequently employed in simulating species distribution and associated ecological modeling. The bioclimatic variables represent seasonality, extreme or limiting environmental elements, annual trends (e.g., mean annual temperature, and annual precipitation), and annual range in temperature and precipitation (e.g., temperature of the coldest and warmest month and precipitation of the wet and dry quarters). The 19 specific bioclimatic variables chosen in this study (Bio1-Bio19) are listed in Table 1.
The current climate is the average data from 1970 to 2000 from the WorldClim version 2.1 (Fick & Hijmans, 2017). BCC-CSM2-MR (Beijing Climate Center Climate System Model; Wu et al., 2019) uses the SSP 1-2.6, SSP 2-4.5, SSP 3-7.0, and SSP 5-8.5 scenarios of the shared socioeconomic pathways (SSPs) adopted by the IPCC 6th Assessment, with a spatial resolution of 5 × 5 km for bioclimatic data in WorldClim v 2.1 . SSP scenarios are based on current national and regional realities and development plan to obtain specific socioeconomic development scenarios.
Relatively strong correlations between the 19 bioclimatic variables could affect the simulation results. The variables were screened to tackle this multicollinearity problem. Spearman correlation analysis and variance inflation factors (VIF) were used to select and keep variables with correlation coefficients less than 0.8 (Abdelaal et al., 2019). Finally, nine bioclimatic variables were retained: Bio2, Bio3, Bio8, Bio10, Bio11, Bio12, Bio15, Bio17, and Bio18.

| Model construction
The ENMeval package in R 4.0.2 (R Core Team, 2020) was used to optimize the MaxEnt model (Muscarella et al., 2014). The block method was used to divide the 212 C. henryi records into four parts, three for training and one for testing. We set the regularization multiplier (RM) to 0.5-4.0 with a 0.5 interval to yield 8 RM. The MaxEnt model provided five feature options: linear (L), quadratic (Q), hinge (H), product (P) and threshold (T) (Nottingham & Pelletier, 2021). We selected six feature combinations: L, LQ, H, LQH, LQHP, and LQHPT. The ENMeval package was used to test the 48 parameter combinations obtained by multiplying 8 RM by 6 feature combinations. The Akaike information criterion correction (AICc) was used to evaluate the fit and complexity of the model (Yan et al., 2021). The difference between training and testing AUC (area under the curve receiver operating characteristic) and the 10% training omission rate was used to evaluate the degree of overfitting of the model. The parameter combination had the minimum delta. The AICc value had been chosen as our optimal parameter to build the model (Steven J Phillips et al., 2017).
In this study, the model's accuracy was assessed by AUC, with a larger value indicating a higher accuracy of the prediction results (Abdelaal et al., 2019). AUC of 0.5-0.6, 0.6-0.7, 0.7-0.8, 0.8-0.9, and >0.9 indicated failure, poor, fair, good, and excellent categories for the predicted results, respectively (Abolmaali et al., 2018;Xie et al., 2021). The survival probability of C. henryi, indicated by the probability p, was reclassified into four categories of habitat suitability categories: poor (p < .25), fair (0.25-0.50), good (0.50-0.75), and excellent (>0.75). The raster tool in QGIS was used to calculate the suitable area. The raster tool in QGIS was used to calculate the suitability areas (QGIS, 2021).

| Geographical distribution of C. henryi in China
The distribution range of C. henryi in China is roughly between

| Key enabling and limiting climatic factors
The AUC value for the MaxEnt was 0.928 ± 0.014 ( Figure 2). Thus, the model was a good predictor of the distribution area of C. henryi. The results (Table 2; Figure 3) indicated that mean diurnal range (Bio2), mean temperature of the coldest quarter (Bio11), precipitation seasonality (Bio15), isothermality (Bio3), and annual precipitation (Bio12) had a strong contribution to predicting the distribution.
The mean temperature of the warmest quarter (Bio10) and mean temperature of the wettest quarter (Bio8) had a moderate influence on the distribution. Precipitation of the warmest quarter (Bio18) and precipitation of the driest quarter (Bio17) had the least effect on predicting the distribution ( Table 2).
Bio2, Bio11, and Bio3 could be subsumed under temperature factors with a considerable cumulative contribution of 81.6%.
Meanwhile, Bio15, Bio12, and Bio18 could be subsumed under moisture factors with a cumulative contribution of 13.5%. Therefore, the temperature had a notably greater influence on distribution than moisture. In addition, the top three bioclimatic variables in terms of permutation importance according to Jackknife were Bio11 (49.3%), Bio12 (12.8%), and Bio18 (9.0%) ( Table 2). The results for both percent contribution and permutation importance suggested that temperature played a greater role than moisture, with Bio11 furnishing a greater weight than the other two bioclimatic variables. Meanwhile, the descriptive statistics of the main bioclimatic parameters in the distribution areas are also displayed in Table 3.
The response curves in Figure 4 show  (Akyol et al., 2020). It is generally accepted that when the distribution probability is >0.50, the corresponding environmental factor value is suitable for the species (Xie et al., 2021).

| Potential distribution range under current climate
The area and distribution pattern of each habitat suitability category under the current climate were evaluated according to the habitat classification criteria ( Table 4; Figure 5). The land areas for excellent, good, fair, and poor suitability were 26.98 × 10 4 km 2 , 53.35 × 10 4 km 2 , 54.36 × 10 4 km 2 , and 825.32 × 10 4 km 2 , respectively. The occurrence of poor suitability was predominant. The suitability area covered 134.69 × 10 4 km 2 or nearly 14% of China's total land area ( Table 4).
Jiangxi Province had the largest excellent-habitat area under the current climate scenario (9.55 × 10 4 km 2 ), accounting for 35.4% of the excellent-habitat area of China ( Table 5).
The distribution pattern of C. henryi under the current climate was simulated by MaxEnt. The results were in high agreement with the actual occurrence record (Figures 1 and 5). The excellent habitats (>0.75) were mainly located in southwestern Zhejiang, northern

| Changes in distribution under different climate scenarios
Compared with the potential distribution area under the current climate scenario, suitable habitats for C. henryi would change under four future climate scenarios in the 2050s and 2070s ( Table 4; Figure 6). Under future climate scenarios, the potential distribution area of the poor tier increases slightly by 1%-2%. In the fair tier, the potential distribution area of each climate scenario reduces notably compared with the current pattern, such as 5.59% (51.32 × 10 4 km 2 ) at RCP8.5-2050s and 18.83% (44.29 × 10 4 km 2 ) at RCP7.0-2070s. In the good tier, the potential distribution area decreased

| Evaluating the MaxEnt model
In this study, the model successfully predicted the suitable habi- More comprehensive species occurrence records can raise the modeling accuracy of species distribution (Feeley & Silman, 2011).
Adequate and representative occurrence records constitute the basis for model construction and improve prediction accuracy (Hernandez et al., 2006). Our MaxEnt modeling yielded a mean AUC over 0.928 ± 0.014 (Figure 2), indicating good prediction results. It has been shown that integrating data from multiple sources could overcome the quality limitations of a single data source and improve prediction accuracy . In addition, using multisource data could overcome the challenge of collecting species information in complex environments and broaden the application scope of ecological niche models (Chen, 2021). However, the method developed in our study focused on the influence of the spatial distance between samples, with less attention to sample size (referring to the 212 high-quality and reliable distribution records of C. henryi selected by the sampling procedures explained in Section 2.1 Collecting species occurrence data) and spatial autocorrelation issues.
This study used environmental data from WorldClim, which were an average of 1970-2000 (Afrianto, 2021). Species distribution patterns have changed significantly in the last 20 years in response to intensifying climate change (Warren et al., 2018). Lacking 20 years of recent climate data may compromise the accuracy of prediction results, which could deviate from the actual situation. Moreover, some discrepancies between the data obtained by interpolation and the actual one are unavoidable (Pellicone et al., 2018). Therefore, we reckoned that the predicted suitable habitats of C. henryi could be broader than the actual coverage.
This study used nine bioclimatic variables to predict the suit-

| Current distribution patterns and limiting factors
Hydrothermal conditions (climate) governed the geographical distribution of vegetation (Hessburg et al., 2019), with thermal conditions influencing the latitudinal change from south to north (in the northern hemisphere) and moisture conditions influencing the regional change from coastal to inland (Noor et al., 2020). with the cold index limiting the northward spread (Tian, 2007). The mean temperature of the driest quarter (winter in the study region) was likely the main factor restricting, for instance, Q. chenii from growing in the north (Li et al., 2016). Among the thermal variables, growing season warmth was most important for Beech (Fagus spp.) F I G U R E 6 MaxEnt modeling of C. henryi based on four future climatechange scenarios in 2050s and 2070s at RCP2.6, RCP4.5, RCP7.0, and RCP8.5. The habitat suitability categories include poor (0-0.25), fair (0.25-0.50), good (0.50-0.75), and excellent (>0.75), denoted by gray, light yellow, orange, and red, respectively.
distribution. However, low winter temperature (coldness and mean temperature for the coldest month) and climatic continentality also influenced Beech occurrence (Fang & Lechowicz, 2006). This study did not find suitable habitats for C. henryi in northern China, probably because of the low winter temperature. In addition, C. henryi had a more confined northward spread than Q. chenii, indicating a lower tolerance to low temperature. Thus, temperature presents a critical environmental factor restricting the northward spread of C. henryi.
The temperature significantly influenced the seed germination for C. henryi. The optimum temperature for C. henryi seed germination is 25°C, below or above which the germination rate and growth of cone chestnut would be inhibited. Meanwhile, seeds could not germinate at 0°C (Li et al., 2020). This temperature control of the germinal stage may constitute a principal limiting factor of its spread. The temperature constraint on the distribution of several Fagaceae species has been demonstrated (Çoban et al., 2020;Conedera et al., 2004;Deng et al., 2018;Fang & Lechowicz, 2006;Li et al., 2016;G. Liu & Fang, 2000;Tulowiecki, 2020;Zhang, Zhu, et al., 2022). Figures 5 and 6 show a tendency of C. henryi to move its suitable habitats toward the eastern and northern regions of China, a process that could be driven by global warming.
However, excessively high or low winter temperatures can also limit the flowering of C. henryi (Ouyang, 2017). This phenomenon could be related to the annual temperature amplitude as one of the most critical factors of plant growth, development, and flowering.
Most plants have to go through the annual cycle of warm-cold-warm phases, requiring the chilling or vernalization treatment for proper phenological expressions (Khodorova & Boitel-Conti, 2013).
Moisture can influence C. henryi distribution with a weaker effect than temperature. Due to the different impacts of the southeast Pacific monsoon and the southwest Indian Ocean monsoon, China's subtropical region is divided into two subregions: the humid east with an average annual precipitation of 1000-2000 mm and the semihumid west of 900-1200 mm . The precipitation conditions in the distribution areas of C. henryi under the current climate scenario aligned with the regional rainfall pattern (Table 3). Changes in the geographical distribution of vegetation from the coastal to inland latitudes were often governed by a corresponding moisture gradient. Seed germination and plant growth are directly influenced by water availability (Li, Li, & Fang, 2018;Liu, Zhao, et al., 2021). Abundant precipitation in winter and spring is a prerequisite for preserving or surviving Chinese endemic plants in East and Central China (Huang et al., 2015). The seeds of most plants mature in the fall. However, dehydration-sensitive seeds tend to die if the winter is too dry (Nepomuceno, 1998). Due to the delayed response of plant physiology to precipitation factors, winter precipitation can exercise control on the dormancy and flowering period of subtropical tree species (Xiao et al., 2021).
In summary, climatic factors can further regulate the distribution pattern of C. henryi by controlling its growth and developmental rhythms (Figure 4). We found that Bio11 restricted the northern spread of C. henryi at the northern boundary of the subtropics (Qinling-Huaihe), Bio10 limited the southern spread toward the tropics, and Bio15 controlled the westward spread. The combined effect of these bioclimatic factors was the range circumscription of C. henryi to subtropical China.

| Differential range changes under future climate scenarios
Under the RCP7.0 scenario (2050s), the excellent habitats of C. henryi occupied the largest area, indicating that moderate climate change could expand the range ( Table 5). Similar to other climate scenarios, its potential suitable habitats would concentrate in Jiangxi, Anhui, and western Hunan. Meanwhile, the excellent habitats in central Guangxi would disappear in the future and tend to migrate northward ( Figure 6).
Under future climate change, the total suitable area of C. henryi would increase and expand northward ( Figure 6). This is mainly However, the excellent-habitat area for C. henryi would decrease under the RCP8.5 future climate scenario, indicating that relatively extreme warming could shrink its range. In recent years in China, warming has led to a significant increase in the extreme high-temperature index in various regions (Jiang et al., 2016) to trim the distribution of C. henryi. In addition, the summer precipitation has been projected to decrease significantly in Central and Western China by the end of the 21st century under a warming scenario, which would induce the loss of some excellent habitats of C. henryi (Zhang, Hu, et al., 2022).
Although the excellent-habitat area of C. henryi increased under various climate scenarios in future, the model results also indicated that too high temperatures were unsuitable for its growth ( Figure 6; Table 4). Even warmth-preferring species may become extinct because the negative impacts of climate change may surpass any potential advantages, such as increased temperature or a minor increase in CO 2 levels (Bussotti et al., 2014;Sarikaya et al., 2022). Therefore, we should be cautious about the impact of climate change on the species' geographical range. Forest and conservation departments should pay more attention to maintaining C. henryi populations in the excellent habitats according to the present findings.

| Implications of germplasm resources on species conservation
Exploring species' responses to future climate change can grasp the ecological risks they face, which are scientifically significant in for-  Schlarbaum et al., 1998). From the 1930s, the Chestnut blight spread over a large area in Europe, causing severe damage to C.
sativa (Ahmad & Baric, 2022). As one of the three major Castanea species endemic to China, the economic and nutritional values of C.
henryi are high. Chestnut blight could reduce the yield and kill many individuals, bringing substantial economic losses and serious ecological hazards (Ye et al., 2022). Therefore, strenuous efforts should be instituted to curb its outbreak, the infectivity of which could be aggravated by global warming. It is an urgent task to accelerate the selection and breeding of new varieties with strong resistance to the disease (Jacobs, 2007). Based on the prediction results in relation to current and future climate, we propose classifying the excellent habitats of C.
henryi into three types, each to receive tailor-made treatments: preservation areas, loss areas, and new areas. First, the preservation areas may become a haven for C. henryi to cope with climate change under different greenhouse gas concentration gradients.
The attention can therefore focus on protection and management.
Second, the reasons for the losses of C. henryi should be analyzed.

| CON CLUS ION
The suitable habitats of C. henryi in China were predicted by MaxEnt modeling under the current climate and four future climate-change scenarios (RCP2.6, RCP4.5, RCP7.0, and RCP8.5), generating good results with a high degree of accuracy. The results of the study were not identical to our initial assumption, which supported the idea that the suitable habitat should shrink under RCP8.5. However, other future climate scenarios were inconsistent with the assumptions.
Under the current climate scenario, the potential biogeographical range of the warmth-preferring C. henryi is mainly found in the sub- Investigation (equal).

ACK N OWLED G M ENTS
We would like to thank the support from Tropical Biodiversity and Bioresource Utilization Laboratory, Qiongtai Normal University (QTPT21-5). We are also grateful to the reviewers for their careful review of this manuscript.

CO N FLI C T O F I NTE R E S T
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

DATA AVA I L A B I L I T Y S TAT E M E N T
The data that support the findings of this study are available online: 4TU Research Data. https://doi.org/10.4121/21257379. The data are not publicly available due to privacy or ethical restrictions.